title: Beyond intention to treat: what is the right question?
abstract: BACKGROUND
  Most methodologists recommend intention-to-treat (ITT) analysis in order to minimize bias. Although ITT analysis provides an unbiased estimate for the effect of treatment assignment on the outcome, the estimate is biased for the actual effect of receiving treatment (active treatment) compared to some comparison group (control). Other common analyses include measuring effects in (1) participants who follow their assigned treatment (Per Protocol), (2) participants according to treatment received (As Treated), and (3) those who would comply with recommended treatment (Complier Average Causal Effect (CACE) as estimated by Principal Stratification or Instrumental Variable Analyses). As each of these analyses compares different study subpopulations, they address different research questions.
  
  
  OBJECTIVE
  For each type of analysis, we review and explain (1) the terminology being used, (2) the main underlying concepts, (3) the questions that are answered and whether the method provides valid causal estimates, and (4) the situations when the analysis should be conducted.
  
  
  METHODS
  We first review the major concepts in relation to four nuances of the clinical question, 'Does treatment improve health?' After reviewing these concepts, we compare the results of the different analyses using data from two published randomized controlled trials (RCTs). Each analysis has particular underlying assumptions and all require dichotomizing adherence into Yes or No. We apply sensitivity analyses so that intermediate adherence is considered (1) as adherence and (2) as non-adherence.
  
  
  RESULTS
  The ITT approach provides an unbiased estimate for how active treatment will improve (1) health in the population if a policy or program is enacted or (2) health of patients if a clinician changes treatment practice. The CACE approach generally provides an unbiased estimate of the effect of active treatment on health of patients who would follow the clinician's advice to take active treatment. Unfortunately, there is no current analysis for clinicians and patients who want to know whether active treatment will improve the patient's health if taken, which is different from the effect in patients who would follow the clinician's advice to take active treatment. Sensitivity analysis for the CACE using two published data sets suggests that the underlying assumptions appeared to be violated.
  
  
  CONCLUSIONS
  There are several methods within each analytical approach we describe. Our analyses are based on a subset of these approaches.
  
  
  CONCLUSIONS
  Although adherence-based analyses may provide meaningful information, the analytical method should match the clinical question, and investigators should clearly outline why they believe assumptions hold and should provide empirical tests of the assumptions where possible.
authors.affiliation: aCentre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada.
authors.forename: Ian
authors.initials: I
authors.lastname: Shrier
authors.forename: Russell J
authors.initials: RJ
authors.lastname: Steele
authors.forename: Evert
authors.initials: E
authors.lastname: Verhagen
authors.forename: Rob
authors.initials: R
authors.lastname: Herbert
authors.forename: Corinne A
authors.initials: CA
authors.lastname: Riddell
authors.forename: Jay S
authors.initials: JS
authors.lastname: Kaufman
mesh_terms: D015982:Bias; D006801:Humans; D057194:Intention to Treat Analysis / Q000379:methods*; D010349:Patient Compliance; D012107:Research Design